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基于整合移动平均自回归和遗传粒子群优化小波神经网络组合模型的交通流预测

殷礼胜 唐圣期 李胜 何怡刚

殷礼胜, 唐圣期, 李胜, 何怡刚. 基于整合移动平均自回归和遗传粒子群优化小波神经网络组合模型的交通流预测[J]. 电子与信息学报, 2019, 41(9): 2273-2279. doi: 10.11999/JEIT181073
引用本文: 殷礼胜, 唐圣期, 李胜, 何怡刚. 基于整合移动平均自回归和遗传粒子群优化小波神经网络组合模型的交通流预测[J]. 电子与信息学报, 2019, 41(9): 2273-2279. doi: 10.11999/JEIT181073
Lisheng YIN, Shengqi TANG, Sheng LI, Yigang HE. Traffic Flow Prediction Based on Hybrid Model of Auto-Regressive Integrated Moving Average and Genetic Particle Swarm Optimization Wavelet Neural Network[J]. Journal of Electronics & Information Technology, 2019, 41(9): 2273-2279. doi: 10.11999/JEIT181073
Citation: Lisheng YIN, Shengqi TANG, Sheng LI, Yigang HE. Traffic Flow Prediction Based on Hybrid Model of Auto-Regressive Integrated Moving Average and Genetic Particle Swarm Optimization Wavelet Neural Network[J]. Journal of Electronics & Information Technology, 2019, 41(9): 2273-2279. doi: 10.11999/JEIT181073

基于整合移动平均自回归和遗传粒子群优化小波神经网络组合模型的交通流预测

doi: 10.11999/JEIT181073
基金项目: 国家自然科学基金(51577046, 61673153),国防科技计划项目(C1120110004, 9140A27020211DZ5102),教育部科学技术研究重大项目(313018),安徽省科技计划重点项目(1301022036)
详细信息
    作者简介:

    殷礼胜:男,1974年生,博士,副教授,研究方向为复杂系统建模;非线性时间序列预测;交通流预测等

    唐圣期:男,1995年生,硕士生,研究方向为交通流预测、智能控制系统

    李胜:男,1993年生,硕士生,研究方向为交通流预测、复杂系统建模

    何怡刚:男,1966年生,博士,教授,研究方向为通讯信道建模与检测、复杂电磁分析与建模等

    通讯作者:

    唐圣期 tsq951024@163.com

  • 中图分类号: TP391, U491.1

Traffic Flow Prediction Based on Hybrid Model of Auto-Regressive Integrated Moving Average and Genetic Particle Swarm Optimization Wavelet Neural Network

Funds: The National Natural Science Foundation of China (51577046, 61673153), The National Defense Advanced Research Project (C1120110004, 9140A27020211DZ5102), The Key Grant Project of Chinese Ministry of Education (313018), Anhui Provincial Science and Technology Foundation of China (1301022036)
  • 摘要: 针对短时交通流数据的非线性和随机性特点,为提高它的预测精度和收敛速度,该文从模型构建和算法两方面提出一种整合移动平均自回归(ARIMA)模型和遗传粒子群算法优化小波神经网络(GPSOWNN)相结合的预测模型和算法。在模型构建方面,将ARIMA模型预测值和灰色关联系数大于0.6的相关性强的前3个时刻的历史数据作为小波神经网络(WNN)的输入,在兼顾历史数据的平稳和非平稳的情况下,进行了模型结构简化。在算法方面,通过遗传粒子群算法对小波神经网络的参数初始值进行最优选取,可使其结果在不易陷入局部最优的条件下加快网络训练收敛速度。实验结果表明,在预测精度方面,该方法的模型明显优于整合移动平均自回归模型和遗传粒子群算法优化小波神经网络,在收敛速度方面,用遗传粒子群算法优化模型明显优于仅用遗传算法优化模型。
  • 图  1  自相关分析

    图  2  偏自相关分析

    图  3  ARIMA(1,1,2)模型预测值和实际值对比

    图  4  交通流实际值各点斜率

    图  5  ARIMA(1, 1, 2)模型预测值和实际值的误差绝对值

    图  6  ARIMA-GPSOWNN组合网络拓扑结构

    图  7  GWNN进化过程

    图  8  GPSOWNN进化过程

    图  9  各模型预测结果与实际值的比较

    图  10  各模型的预测误差对比

    表  1  4组模型的AIC值

    模型AIC值
    ARIMA(1, 1, 2)7.210306
    ARIMA(2, 1, 2)7.426953
    ARIMA(1, 1, 3)7.250197
    ARIMA(2, 1, 3)7.509981
    下载: 导出CSV

    表  2  实时时刻与历史时刻的灰色关联系数

    交通流历史时刻时间
    序列${x_{k - i}}$ ($i$=1, 2, ···, 7)
    与${x_k}$的灰色关联系数
    ${x_{k - 1}}$0.8271
    ${x_{k - 2}}$0.8155
    ${x_{k - 3}}$0.6546
    ${x_{k - 4}}$0.5346
    ${x_{k - 5}}$0.5146
    ${x_{k - 6}}$0.5126
    ${x_{k - 7}}$0.5453
    下载: 导出CSV

    表  3  3种模型的总标准误差

    模型总标准误差
    GPSOWNN和ARIMA组合模型294.5303
    GPSOWNN模型369.7026
    ARIMA模型459.0784
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-11-22
  • 修回日期:  2019-03-29
  • 网络出版日期:  2019-04-03
  • 刊出日期:  2019-09-10

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